MIT 6.S191 (2020): Generalizable Autonomy for Robot Manipulation

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MIT Introduction to Deep Learning 6.S191: Lecture 8
Generalizable Autonomy for Robot Manipulation
Lecturer: Animesh Garg (NVIDIA & University of Toronto)
January 2020

Lecture Outline
0:00 - Introduction
3:45 - Achieving generalizable autonomy
4:19 - Leveraging imitation learning
6:08 - Learning visuo-motor policies
13:09 - Learning skills
16:38 - Off-policy RL + AC-Teach
22:02 - Compositional planning
27:20 - Model-based RL
34:37 - Leveraging task structure
36:35 - Neural task programming (NTP)
43:04 - Data for robotics
44:24 - RoboTurk
45:54 - Summary

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powerful lecture, I like kind of things like how they think and ask from themselves right questions.

darkhanzholtayev
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because things change so fast this recent high level view is a great jumping off point for further study. thank you!

cagneymoreau
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3:20 Any one knows the video? link plz. thanks.

hanyanglee
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25:57 Machine Learning in Early Human State!!! Its learning to use the tools what ours ancestors used.!!!

Avisheknandi
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Lol the subtitles at 40:09. Kinda ironic :D

minkbag
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I have a question but before that first, I watched all videos till this one I grabbed like every part but couldn't understand them. Like in the first 6 lectures we got brief about the techniques of deep learning and a bunch of other stuff but these guest lectures couldn't bring anything from this, we learned beginner than directly go for the expert. Coming to the question is I would like to know what are the things needed to understand briefly, these upcoming technologies and these guest lectures? either mathematical knowledge or the knowledge about these technologies? If anyone could answer this query of mine I would be very much thankful.

RishabhJain-ukqt
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It seems no one cares about robot manipulation, given that there are hundreds or even thousands of comments in other lectures.

zuyuanzhu
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my dog is smarter than these robots lol.

zeesamuel